Intelligent Analysis of Environmental Data (S4 ENVISA
Workshop 2009) 18-20 June 2009, University of Palermo, Italy


Intel...
Introduction

Analysis concerning earthquake events, are normally strictly
related to damage survey.
It is evident that do...
Introduction

Why Rough Set Analysis for the analysis of
 earthquake events?

o    The aim is to verify the dependence of ...
Rough set
                                 Information System
                                          IS = (U, A)
Let U ...
Rough set

                                                                                            U       a1 a2 a3
  ...
Rough set

                Decision System                                                         U       a1 a2 a3 d1
   ...
Rough set
                             Indiscernibility Relation
                                       ∀ B ⊂ A → Ind (B)
...
Rough set

                                                                            Lower Approximation
               ...
Rough set
                                         Rough membership
In order to have an idea about how much an object x be...
Rough set
                                                      Reducts
  A reduct eliminate redundant attributes
  A redu...
Rough set
                                                      Reducts
                      Color                  Size ...
Rough set
     U = {x1, x2, x3, x4, x5, x6, x7, x8}


     A = {color, size, shape}
           color(green, blue, red, yel...
Rough set
  U/IND(A) =           {(x1, x5), (x2, x8), (x3), (x4), (x6), (x7)}

  U/ IND(A –{color}) = {(x1,               ...
Case Study

                                                                     Rapolla




  Earthquake 1930

  Building...
Case Study




Intelligent analysis for historical macroseismic damage scenarios
                                         ...
Case Study
                              GENERAL DATA AND TECHNICAL REPORT

   N. fascicle     N. tech. report            ...
Case Study
       a lot of information
       about reconstruction




                                                   ...
Case Study
Data concerning information about the damage, the post-seismic
repairing procedures with buildings techniques d...
Case Study

  Walls demolition
  Floors demolition
  Vault demolition
  New wall
  New Floors
  Toothing projects
  Sheari...
Case Study




Intelligent analysis for historical macroseismic damage scenarios
                                         ...
Case Study




Intelligent analysis for historical macroseismic damage scenarios
                                         ...
Case Study



                                                                                                    }  CONDI...
Case Study




Intelligent analysis for historical macroseismic damage scenarios
                                         ...
Case Study




Intelligent analysis for historical macroseismic damage scenarios
                                         ...
Case Study




Intelligent analysis for historical macroseismic damage scenarios
                                         ...
Case Study




Intelligent analysis for historical macroseismic damage scenarios
                                         ...
Case Study
There is a certain number of rules (25/88) that present a
clear discrepancy into damage level attribution:

The...
Case Study
Changes in damage classification seem not to be due to
 voluntary human influences (e.g. acquaintance with
 tec...
Case Study

o Feature of damage description: during initial post-seismic
  phases, report of damage included improvements ...
Future developments
                                         New study area




 It is known that during
 an    earthquake...
Future developments

     Compare Rough Set results with other intelligent methods
       using Visual Analytics:

     o ...
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Intelligent analysis for historical macroseismic damage scenarios Fabrizio Gizzi, Nicola Masini Maria Rosaria Potenza, Maria Danese, Cinzia Zotta - Archaeological and monumental heritage institute, National Research Council, Potenza (Italy), Lucia Tilio,

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Intelligent analysis for historical macroseismic damage scenarios - Fabrizio Gizzi, Nicola Masini Maria Rosaria Potenza, Maria Danese, Cinzia Zotta - Archaeological and monumental heritage institute, National Research Council, Potenza (Italy),
Lucia Tilio, Beniamino Murgante - Laboratory of Urban and Territorial Systems, University of Basilicata (Italy)
Intelligent Analysis of Environmental Data (S4 ENVISA Workshop 2009)

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Intelligent analysis for historical macroseismic damage scenarios Fabrizio Gizzi, Nicola Masini Maria Rosaria Potenza, Maria Danese, Cinzia Zotta - Archaeological and monumental heritage institute, National Research Council, Potenza (Italy), Lucia Tilio,

  1. 1. Intelligent Analysis of Environmental Data (S4 ENVISA Workshop 2009) 18-20 June 2009, University of Palermo, Italy Intelligent analysis for historical macroseismic damage scenarios Fabrizio Gizzi, Nicola Masini Maria Rosaria Potenza, Cinzia Zotta, Archaeological and monumental heritage institute, National Research Council, Italy Lucia Tilio, Maria Danese, Beniamino Murgante Laboratory of Urban and Territorial Systems, University of Basilicata, Italy
  2. 2. Introduction Analysis concerning earthquake events, are normally strictly related to damage survey. It is evident that documentary sources concerning urban historical damage can provide useful information for seismic microzonation. This research concerns historical earthquake (1930) damage related to towns of a seismic area of southern Italy (Vulture district, Basilicata). 4,000 dossiers compiled by the Special Office of Civil Engineers have been analyzed. Intelligent analysis for historical macroseismic damage scenarios Intelligent Analysis of Environmental Data (S4 ENVISA Fabrizio Gizzi, Nicola Masini Maria Rosaria Potenza, Maria Danese, Workshop 2009) 18-20 June 2009, Palermo, Italy Cinzia Zotta, Lucia Tilio, Beniamino Murgante
  3. 3. Introduction Why Rough Set Analysis for the analysis of earthquake events? o The aim is to verify the dependence of the damage level attribution to each building from some socio- economical local dynamics o All available variables have been take into account and searching some patterns, able to create a cross-data control. Intelligent analysis for historical macroseismic damage scenarios Intelligent Analysis of Environmental Data (S4 ENVISA Fabrizio Gizzi, Nicola Masini Maria Rosaria Potenza, Maria Danese, Workshop 2009) 18-20 June 2009, Palermo, Italy Cinzia Zotta, Lucia Tilio, Beniamino Murgante
  4. 4. Rough set Information System IS = (U, A) Let U be a nonempty finite set of objects called the universe U = { x1 , x 2 , x 3 , x 4 , x 5 , x 6 ,............, xn } Let A be a nonempty finite set of attributes A = {A 1 , A 2 , A 3 } ∀ a ∈ A → Va = value set (domain of attribute) V1 = {1 ,2, 3 } V2 = {1 , 2} V3 = {1 ,2, 3, 4} Intelligent analysis for historical macroseismic damage scenarios Intelligent Analysis of Environmental Data (S4 ENVISA Fabrizio Gizzi, Nicola Masini Maria Rosaria Potenza, Maria Danese, Workshop 2009) 18-20 June 2009, Palermo, Italy Cinzia Zotta, Lucia Tilio, Beniamino Murgante
  5. 5. Rough set U a1 a2 a3 x1 2 1 3 Information System X2 3 2 1 X3 2 1 3 X4 2 2 3 f : U → Va a informatio n function X5 1 1 4 X6 1 1 2 X7 3 2 1 X8 1 1 4 X9 2 1 3 x10 3 2 1 Intelligent analysis for historical macroseismic damage scenarios Intelligent Analysis of Environmental Data (S4 ENVISA Fabrizio Gizzi, Nicola Masini Maria Rosaria Potenza, Maria Danese, Workshop 2009) 18-20 June 2009, Palermo, Italy Cinzia Zotta, Lucia Tilio, Beniamino Murgante
  6. 6. Rough set Decision System U a1 a2 a3 d1 x1 2 1 3 1 A decision system is an information system in which X2 3 2 1 4 the values of a special X3 2 1 3 5 decision attribute classify X4 2 2 3 2 the cases X5 1 1 4 2 X6 1 1 2 4 DS = (U, A ∪ d ) d≠A X7 3 2 1 1 other attributes a ∈ A - { d} X8 1 1 4 2 X9 2 1 3 3 Conditiona l Attributes x10 3 2 1 2 Intelligent analysis for historical macroseismic damage scenarios Intelligent Analysis of Environmental Data (S4 ENVISA Fabrizio Gizzi, Nicola Masini Maria Rosaria Potenza, Maria Danese, Workshop 2009) 18-20 June 2009, Palermo, Italy Cinzia Zotta, Lucia Tilio, Beniamino Murgante
  7. 7. Rough set Indiscernibility Relation ∀ B ⊂ A → Ind (B) xi e x j are Ind (B) → b(xi ) = b(x j ) ∀ b∈B o The equivalence class of Ind (B) is U/A a1 a2 a3 called ELEMENTARY SET in B (X1 , X3 , X9 ) 2 1 3 (X2 , X7 , X10 ) 3 2 1 o For any element xi of U, the (X4) 2 2 3 EQUIVALENCE CLASS of R (X5 , X8 ) 1 1 4 containing xi in relation Ind (B) will (X6) 1 1 2 be denoted by [Xi] ind B (X7) 3 2 1 Intelligent analysis for historical macroseismic damage scenarios Intelligent Analysis of Environmental Data (S4 ENVISA Fabrizio Gizzi, Nicola Masini Maria Rosaria Potenza, Maria Danese, Workshop 2009) 18-20 June 2009, Palermo, Italy Cinzia Zotta, Lucia Tilio, Beniamino Murgante
  8. 8. Rough set Lower Approximation LX = { xi ∈ U [ xi ] ind ( B ) ⊂ X } Equivalence classes Upper Approximation { UX = xi ∈ U [ xi ] ind ( B ) ∩ X ≠ ∅ } Boundary Region BX = UX − LX Accuracy If BX = ∅ then the set X is Crisp µ B ( X ) = card ( LX ) / card (UX ) If BX ≠ ∅ then the set X is Intelligent analysis for historical macroseismic damage scenarios Rough Intelligent Analysis of Environmental Data (S4 ENVISA Fabrizio Gizzi, Nicola Masini Maria Rosaria Potenza, Maria Danese, Workshop 2009) 18-20 June 2009, Palermo, Italy Cinzia Zotta, Lucia Tilio, Beniamino Murgante
  9. 9. Rough set Rough membership In order to have an idea about how much an object x belongs to X we define rough membership. [ xi ] ind ( B ) ∩ X µ ind ( B ) → [0,1] and µ ( x) : U  ind ( B ) ( x) = X X [ xi ] ind ( B ) The rough membership function quantifies the degree of relative overlap between the set X and the equivalence class to which x belongs. Intelligent analysis for historical macroseismic damage scenarios Intelligent Analysis of Environmental Data (S4 ENVISA Fabrizio Gizzi, Nicola Masini Maria Rosaria Potenza, Maria Danese, Workshop 2009) 18-20 June 2009, Palermo, Italy Cinzia Zotta, Lucia Tilio, Beniamino Murgante
  10. 10. Rough set Reducts A reduct eliminate redundant attributes A reduct is a minimal set of attributes (from the whole attributes set) that preserves the partitioning of the of U and therefore the original classes. Intelligent analysis for historical macroseismic damage scenarios Intelligent Analysis of Environmental Data (S4 ENVISA Fabrizio Gizzi, Nicola Masini Maria Rosaria Potenza, Maria Danese, Workshop 2009) 18-20 June 2009, Palermo, Italy Cinzia Zotta, Lucia Tilio, Beniamino Murgante
  11. 11. Rough set Reducts Color Size Shape Accept x1 G Small Square Yes x2 B Medium Triangular No x3 R Small Rectangular No x4 G Medium Rectangular Yes x5 G Small Square Yes x6 Y Large Round No x7 Y Medium Triangular Yes x8 B Medium Triangular No Intelligent analysis for historical macroseismic damage scenarios Intelligent Analysis of Environmental Data (S4 ENVISA Fabrizio Gizzi, Nicola Masini Maria Rosaria Potenza, Maria Danese, Workshop 2009) 18-20 June 2009, Palermo, Italy Cinzia Zotta, Lucia Tilio, Beniamino Murgante
  12. 12. Rough set U = {x1, x2, x3, x4, x5, x6, x7, x8} A = {color, size, shape} color(green, blue, red, yellow) size(small, large, medium) shape(square, round, triangular, rectangular) U/color = {(x1, x4, x5), (x2, x8), (x3), (x6, x7)} U/size = {(x1, x3, x5), (x6), (x2, x4, x7 , x8)} Intelligent analysis for historical macroseismic damage scenarios U/shape = {(x , x ), (x ), (x , x , x8), Workshop x4 )} June 2009, Palermo, Italy (x3 , 2009) 18-20 Intelligent Analysis of Environmental Data (S4 ENVISA Fabrizio Gizzi, Nicola Masini Maria Rosaria Potenza, Maria Danese, Cinzia Zotta, Lucia Tilio, Beniamino Murgante 6 1 5 2 7
  13. 13. Rough set U/IND(A) = {(x1, x5), (x2, x8), (x3), (x4), (x6), (x7)} U/ IND(A –{color}) = {(x1, x5), (x2, x7 , x8), (x3), (x4) (x6)} ≠ U/IND(A) U/ IND(A –{size}) = {(x1, x5), (x2, x8), (x3), (x4), (x6), (x7)} = U/IND(A) U/ IND(A –{shape}) = {(x1, x5), (x2, x8), (x3), (x4), (x6), (x7)} = U/IND(A) RED(A) = {(color, size), (color, shape)} CORE(A) = {color} Intelligent analysis for historical macroseismic damage scenarios Intelligent Analysis of Environmental Data (S4 ENVISA Fabrizio Gizzi, Nicola Masini Maria Rosaria Potenza, Maria Danese, Workshop 2009) 18-20 June 2009, Palermo, Italy Cinzia Zotta, Lucia Tilio, Beniamino Murgante
  14. 14. Case Study Rapolla Earthquake 1930 Buildings damage survey 738 Attributes 37 Which relationship between damage and reconstruction Intelligent analysis for historical macroseismic damage scenarios Intelligent Analysis of Environmental Data (S4 ENVISA Fabrizio Gizzi, Nicola Masini Maria Rosaria Potenza, Maria Danese, Workshop 2009) 18-20 June 2009, Palermo, Italy Cinzia Zotta, Lucia Tilio, Beniamino Murgante
  15. 15. Case Study Intelligent analysis for historical macroseismic damage scenarios Intelligent Analysis of Environmental Data (S4 ENVISA Fabrizio Gizzi, Nicola Masini Maria Rosaria Potenza, Maria Danese, Workshop 2009) 18-20 June 2009, Palermo, Italy Cinzia Zotta, Lucia Tilio, Beniamino Murgante
  16. 16. Case Study GENERAL DATA AND TECHNICAL REPORT N. fascicle N. tech. report Owner Synthetic cadastral data Busta Fasc Ditta Partita Mappale Address Indirizzo Neighbours Confini dell'immobile Neighbouring parcels Particelle confinanti urban rural b Contractor Impresa Public building YES NO DETAILED CADASTRAL DATA Plans Sections YES NO YES NO Parcel sub U G IF IIF Cadastral rent Floors Form used in order to record Mappale sub Sott PT IP IIP Imponibile fabbr U GF 1F 2F 3F Revocation of housing subsidies Expiry YES NO Works carried out by national government and to analyse the documentary data Imponibile totale fabbr YES NO MAIN TECHNICAL REPORT Supplementary technical report Date Cost Decree PP N N pp DATA PP imp Proposto: Date PP DMLP data Date PS data N. PP DMLP N Cost: PS importo TEST (acceptance of work) CC data Property value Valore immobile Supplementary subsidy Work time Stoppage Work costs Date: PSS data From Inizio lavori From Sospensione dal CC imp1 Cost : PSS importo To Fine lavori To Sospensione al Ministry comunication Prize for quick execution works % PA percent DAMAGE Total cost CM approvato Date Data richiesta ditta Direct Date CM data1 USGCM date Data proposta Genio YES NO Subsidy CM sussidio Year income Reddito annuo Date CM data2 Concession date Data concessione Ministero NOTES Note Intelligent analysis for historical macroseismic damage scenarios Intelligent Analysis of Environmental Data (S4 ENVISA Fabrizio Gizzi, Nicola Masini Maria Rosaria Potenza, Maria Danese, Workshop 2009) 18-20 June 2009, Palermo, Italy Cinzia Zotta, Lucia Tilio, Beniamino Murgante
  17. 17. Case Study a lot of information about reconstruction budget amount, effective expense, presence of some interventions, building value, annual income and so on… Intelligent analysis for historical macroseismic damage scenarios Intelligent Analysis of Environmental Data (S4 ENVISA Fabrizio Gizzi, Nicola Masini Maria Rosaria Potenza, Maria Danese, Workshop 2009) 18-20 June 2009, Palermo, Italy Cinzia Zotta, Lucia Tilio, Beniamino Murgante
  18. 18. Case Study Data concerning information about the damage, the post-seismic repairing procedures with buildings techniques description of the housing units and technical-economic-administrative data. Building ID Start Work Date Reference – Map End Work Date Reference – Envelope Real estate values of Building Reference – Folder Owner Annual Income Reference – Street Adoption of tie-beam Building demolition Roof rebuilding Public Building Cracks rebuilding Religious Building Test date Withdrawn subvention Estimated costs of works Assessment of damage Date Costs of works accounted Costs of works Effectively Funded Intelligent analysis for historical macroseismic damage scenarios Intelligent Analysis of Environmental Data (S4 ENVISA Fabrizio Gizzi, Nicola Masini Maria Rosaria Potenza, Maria Danese, Workshop 2009) 18-20 June 2009, Palermo, Italy Cinzia Zotta, Lucia Tilio, Beniamino Murgante
  19. 19. Case Study Walls demolition Floors demolition Vault demolition New wall New Floors Toothing projects Shearing stress of masonry (technical procedure for walls rebuilding) Cuci-Scuci (technical procedure for walls rebuilding) Damage description Declared Destroyed (if the building was damaged and declared not reconstructable) Damage class EMS Presence of caves under the building Intelligent analysis for historical macroseismic damage scenarios Intelligent Analysis of Environmental Data (S4 ENVISA Fabrizio Gizzi, Nicola Masini Maria Rosaria Potenza, Maria Danese, Workshop 2009) 18-20 June 2009, Palermo, Italy Cinzia Zotta, Lucia Tilio, Beniamino Murgante
  20. 20. Case Study Intelligent analysis for historical macroseismic damage scenarios Intelligent Analysis of Environmental Data (S4 ENVISA Fabrizio Gizzi, Nicola Masini Maria Rosaria Potenza, Maria Danese, Workshop 2009) 18-20 June 2009, Palermo, Italy Cinzia Zotta, Lucia Tilio, Beniamino Murgante
  21. 21. Case Study Intelligent analysis for historical macroseismic damage scenarios Intelligent Analysis of Environmental Data (S4 ENVISA Fabrizio Gizzi, Nicola Masini Maria Rosaria Potenza, Maria Danese, Workshop 2009) 18-20 June 2009, Palermo, Italy Cinzia Zotta, Lucia Tilio, Beniamino Murgante
  22. 22. Case Study } CONDITIONAL PART } ASSIGNMENT Intelligent analysis for historical macroseismic damage scenarios Intelligent Analysis of Environmental Data (S4 ENVISA Fabrizio Gizzi, Nicola Masini Maria Rosaria Potenza, Maria Danese, Workshop 2009) 18-20 June 2009, Palermo, Italy Cinzia Zotta, Lucia Tilio, Beniamino Murgante
  23. 23. Case Study Intelligent analysis for historical macroseismic damage scenarios Intelligent Analysis of Environmental Data (S4 ENVISA Fabrizio Gizzi, Nicola Masini Maria Rosaria Potenza, Maria Danese, Workshop 2009) 18-20 June 2009, Palermo, Italy Cinzia Zotta, Lucia Tilio, Beniamino Murgante
  24. 24. Case Study Intelligent analysis for historical macroseismic damage scenarios Intelligent Analysis of Environmental Data (S4 ENVISA Fabrizio Gizzi, Nicola Masini Maria Rosaria Potenza, Maria Danese, Workshop 2009) 18-20 June 2009, Palermo, Italy Cinzia Zotta, Lucia Tilio, Beniamino Murgante
  25. 25. Case Study Intelligent analysis for historical macroseismic damage scenarios Intelligent Analysis of Environmental Data (S4 ENVISA Fabrizio Gizzi, Nicola Masini Maria Rosaria Potenza, Maria Danese, Workshop 2009) 18-20 June 2009, Palermo, Italy Cinzia Zotta, Lucia Tilio, Beniamino Murgante
  26. 26. Case Study Intelligent analysis for historical macroseismic damage scenarios Intelligent Analysis of Environmental Data (S4 ENVISA Fabrizio Gizzi, Nicola Masini Maria Rosaria Potenza, Maria Danese, Workshop 2009) 18-20 June 2009, Palermo, Italy Cinzia Zotta, Lucia Tilio, Beniamino Murgante
  27. 27. Case Study There is a certain number of rules (25/88) that present a clear discrepancy into damage level attribution: The analysis permits the identification of such discrepancy and a possible interpretation: differences in damage distribution are not spatially clusterized, but they concerns areas having different social and building features (rich and poor owners, big and small housing, building well preserved and lacking of maintenance ect.) Intelligent analysis for historical macroseismic damage scenarios Intelligent Analysis of Environmental Data (S4 ENVISA Fabrizio Gizzi, Nicola Masini Maria Rosaria Potenza, Maria Danese, Workshop 2009) 18-20 June 2009, Palermo, Italy Cinzia Zotta, Lucia Tilio, Beniamino Murgante
  28. 28. Case Study Changes in damage classification seem not to be due to voluntary human influences (e.g. acquaintance with technicians to get increase of damage attribution by favoritism) rather differences may be imputable to other factors, among which: o Rough initial inspection of buildings (e.g. only some rooms were surveyed, damage assessment was carried out from outside of buildings). o Different vocational training of engineers entrusted to survey affected housing units. Intelligent analysis for historical macroseismic damage scenarios Intelligent Analysis of Environmental Data (S4 ENVISA Fabrizio Gizzi, Nicola Masini Maria Rosaria Potenza, Maria Danese, Workshop 2009) 18-20 June 2009, Palermo, Italy Cinzia Zotta, Lucia Tilio, Beniamino Murgante
  29. 29. Case Study o Feature of damage description: during initial post-seismic phases, report of damage included improvements and/or extension works unrelated to the seismic event. o Incompleteness of descriptive data: administrative/technical parametric information on which the rules are based on, sometimes supply more constraints of some very concise description of effects given by the engineer surveys. Intelligent analysis for historical macroseismic damage scenarios Intelligent Analysis of Environmental Data (S4 ENVISA Fabrizio Gizzi, Nicola Masini Maria Rosaria Potenza, Maria Danese, Workshop 2009) 18-20 June 2009, Palermo, Italy Cinzia Zotta, Lucia Tilio, Beniamino Murgante
  30. 30. Future developments New study area It is known that during an earthquake the damage to buildings with comparable features can differ enormously between points. In a wider area it could be interesting to analyze also effects of geological surface. Intelligent analysis for historical macroseismic damage scenarios Intelligent Analysis of Environmental Data (S4 ENVISA Fabrizio Gizzi, Nicola Masini Maria Rosaria Potenza, Maria Danese, Workshop 2009) 18-20 June 2009, Palermo, Italy Cinzia Zotta, Lucia Tilio, Beniamino Murgante
  31. 31. Future developments Compare Rough Set results with other intelligent methods using Visual Analytics: o Multiform Bivariate Matrix o Self-Organising Map (SOM) o Parallel Coordinates Plot (PCP) Intelligent analysis for historical macroseismic damage scenarios Intelligent Analysis of Environmental Data (S4 ENVISA Fabrizio Gizzi, Nicola Masini Maria Rosaria Potenza, Maria Danese, Workshop 2009) 18-20 June 2009, Palermo, Italy Cinzia Zotta, Lucia Tilio, Beniamino Murgante
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